Overview

This model, difficulty_sorting_random_seed_code, is a specialized fine-tuned variant of the Qwen/Qwen2.5-7B-Instruct base model. It has been adapted for specific tasks related to difficulty sorting and random seed code, suggesting an application in areas like code generation, analysis, or educational tools where these concepts are relevant.

Training Details

The model was trained using the following key hyperparameters:

Learning Rate: 1e-05
Batch Size: 1 (train), 8 (eval)
Gradient Accumulation Steps: 6, leading to a total effective training batch size of 96.
Optimizer: AdamW with default betas and epsilon.
Scheduler: Cosine learning rate scheduler with a 0.1 warmup ratio.
Epochs: 3.0

Intended Use

While specific intended uses and limitations require more detailed information, its fine-tuning on a dataset related to "difficulty sorting random seed code" indicates its potential utility in tasks that involve:

Analyzing or generating code snippets with varying difficulty levels.
Managing or predicting outcomes based on random seeds in code.

Further details on its performance and specific applications would require additional information from the model developer.